Large-Scale Multidisciplinary Design Optimization of an eVTOL Aircraft using Comprehensive Analysis

Sarojini, Darshan; Ruh, Marius L.; Joshy, Anugrah Jo; Yan, Jiayao; Ivanov, Alexander K.; Scotzniovsky, Luca; Fletcher, Andrew H.; Orndorff, Nicholas C.; Sperry, Mark; Gandarillas, Victor E.; Asher, Issac; Chambers, Jeffrey T.; Gill, Hyunjune; Lee, Seongkyu; Cheng, Zeyu; Rodriguez, Gabriel; Zhao, Shuofeng; Mi, Chris; Nascenzi, Thomas; Cuatt, Timothy; Winter, Tyler; Guibert, Alexandre T.; Cronk, Ashley; Kim, Hyunsun; Meng, Shirley; Hwang, John T.

doi:10.2514/6.2023-0146

Cited by 21 publications

(4 citation statements)

References 95 publications

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“…For the design of a 19-seater turboprop airplane, we have compiled a dataset comprising aircraft such as Let L410 Turbolet, Dornier 228, DHC-6 Havilland Canada Twin, Beechcraft 1900, Embraer EMB-110 Bandeirante, Jetstream 32, Fairchild Metroliner III, SC7-3 Skyvan, Piper PA-42.1000 Cheyenne, Antonov An-28, and Beechcraft C99 [29][28] [27][26] [25]. In the conceptual design phase, calculations are made to determine the minimum weight of the airplane and the required fuel weight to fulfil the mission, based on mission specifications and customer requirements [30]. This meticulous process ensures that the airplane's design closely aligns with the operational needs and performance expectations established at the outset.…”

Section: Weight Estimationmentioning

confidence: 99%

Intelligent Design Methodology: A Fusion of Fuzzy Logic, Axiomatic Design, and Meta-Heuristic Optimization for Airplane Design

Shafieenejad,

Nourianpour,

Banitalebidehkordi

2024

Preprint

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This article presents a novel methodology for airplane design, integrating fuzzy logic, axiomatic design, and meta-heuristic optimization algorithms tailored for general aviation projects. The primary contribution of this study lies in the synthesis of conventional design practices with fuzzy and axiomatic decision-making and powerful meta-heuristic optimization algorithms. Five distinct phases are delineated in the intelligent design process of the airplane. Initially, airplane design parameters are established based on conventional methods. Subsequently, fuzzy logic is employed to make decisions based on these parameters in accordance with the conventional design criteria. Additionally, the axiomatic design method is utilized to identify values crucial to the design process. Furthermore, meta-heuristic optimization algorithms are deployed at various stages of the proposed novel algorithm to attain optimal design points. Notably, four robust optimization algorithms, Particle Swarm Optimization(PSO), Artificial Bee Colony(ABC), Firefly Algorithm(FA), and Gray Wolf Optimization(GWO), are utilized for verification purposes. The adoption of robust, precise, and intelligent systematic approaches in airplane design is deemed imperative to meet stringent requirements effectively. Ultimately, the design values obtained lead to the optimal configuration of a 19-seat airplane, which undergoes rigorous comparison, verification, and validation against existing airplane models. In summary, the fusion of fuzzy logic, axiomatic design, and potent meta-heuristic optimization algorithms presents a new and innovative methodology in airplane design, promising significant advancements in the field of airplane designing.

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Section: Weight Estimationmentioning

confidence: 99%

Intelligent Design Methodology: A Fusion of Fuzzy Logic, Axiomatic Design, and Meta-Heuristic Optimization for Airplane Design

Shafieenejad,

Nourianpour,

Banitalebidehkordi

2024

Preprint

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“…A point [𝑥 𝑘 , 𝑦 ′ 𝑘 , 𝜓 ′ 𝑘 , 𝜆 𝑘 ] 𝑇 is considered sufficiently optimal in Algorithm 4 if it satisfies the following first-order optimality conditions to specified tolerances: C ¯(𝑥, 𝑦) ≥ 0, 𝜆 ≥ 0, 𝜆 𝑇 C(𝑥, 𝑦) = 0, and 𝑚 𝑧 = 𝑑𝑚/𝑑𝑧 = 0, (16) where C ¯(𝑥, 𝑦) = (C(𝑥, 𝑦), R (𝑥, 𝑦), −R (𝑥, 𝑦)) is the concatenated vector of all constraints in all-inequality form. It can be shown that this set of optimality conditions in an MFS algorithm is equivalent to the optimality conditions in an RS algorithm.…”

Section: Computational Convergence Conditionsmentioning

confidence: 99%

“…The motor model is based on a differentiable method [15] proposed by Cheng et al for the low-fidelity analysis of a PMSM. The motor model employed in this study was originally developed as a part of a large-scale system model of an eVTOL aircraft [16]. In our study, we maximize the efficiency of a PMSM subject to lower and upper bounds on the length and diameter of the motor.…”

Section: A Motor Optimizationmentioning

confidence: 99%

An SQP algorithm based on a hybrid architecture for accelerating optimization of large-scale systems

Joshy

Dunn

Sperry

et al. 2023

AIAA AVIATION 2023 Forum

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The optimization of large-scale and multidisciplinary engineering systems is now prevalent in many fields, exemplified in areas such as aircraft, satellite, and wind turbine design. The rise of advanced modeling frameworks has expanded the scope of large-scale optimization techniques across various research domains. However, novel applications have emerged that pose efficiency-related computational challenges to the existing methods employed in large-scale, gradient-based optimization. The authors previously proposed a new paradigm for accelerating the optimization of large-scale and complex-engineered systems, laying the groundwork for a new approach. The new paradigm is based on a hybrid optimization architecture called SURF which stands for strong unification of reduced-space and full-space. SURF has the potential to expedite optimization of models with state variables that are iteratively computed by solving nonlinear systems. This paper extends the existing paradigm by providing new theoretical results that unify the reduced-space and full-space algorithms in a practical optimization setting that considers line searches and quasi-Newton methods. We also present a practical, SQP-based SURF algorithm that can be applied to general, inequality-constrained problems. The new algorithm also includes an adaptive hybrid selection strategy for robust convergence and faster solutions. We test the new algorithm on a low-fidelity motor optimization problem and a wind farm layout optimization problem to validate the optimization results, and to demonstrate its computational benefits. In one of the problems, SURF was able to speed up the traditional optimization by approximately 25 percent. In the other problem, SURF was able to converge to a better optimal solution. I. Nomenclature 𝜕𝐹 𝜕𝑥= partial derivative of a function 𝐹 with respect to a variable 𝑥 d 𝑓 d𝑥 = total derivative of a function 𝐹 with respect to a variable 𝑥

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“…With the adjoint method, the cost of derivative computation scales with the number of outputs rather than the number of design variables; for optimization problems with a single objective, updating the design variables using the adjoint method is a more efficient approach (Martins and Ning, 2022;Martins and Hwang, 2013;Gray et al, 2019). This approach has been applied to efficiently optimize large systems with many degrees of freedom across various disciplines, such as topology optimization (Yan et al, 2022), aerodynamic shape optimization (Chauhan and Martins, 2021) and multidisciplinary aircraft design optimization (Sarojini et al, 2023). More recently, this approach has been applied to electric motor design; Babcock et al showed the feasibility of using adjoint-based derivative computation for motor design optimization with electro-thermal coupling (Babcock et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Geometric design of electric motors using adjoint-based shape optimization

Scotzniovsky,

Xiang,

Cheng

et al. 2024

Optim Eng

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Interest in electric aircraft has increased due to developments in electric propulsion technology and concerns regarding aircraft carbon emissions. The emerging urban air mobility (UAM) industry aims to provide convenient short-range air travel using electric aircraft. An important factor in the design of electric aircraft is the modeling and design of electric motors. The many degrees of freedom in electric motor design makes it a complex design problem. To mitigate this complexity, we have developed a novel adjoint-based electric motor design methodology using finite-element electromagnetic analysis and PDE-based mesh warping with exact derivatives. This paper highlights the approach and details of the proposed method and presents results from its application to a representative motor design problem. The optimization results in a 35% decrease in motor mass and a 3% increase in efficiency in comparison to the baseline design. These results demonstrate the efficacy of an adjoint-based optimization approach with exact analytical derivatives for electric motor design.

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Large-Scale Multidisciplinary Design Optimization of an eVTOL Aircraft using Comprehensive Analysis

Abstract: This paper presents a framework under development for enabling large-scale multi-fidelity modeling and optimization of electric vertical takeoff and landing concepts (eVTOL). The key features of the framework are a geometry-centric approach to multidisciplinary design

Cited by 21 publications

References 95 publications

Intelligent Design Methodology: A Fusion of Fuzzy Logic, Axiomatic Design, and Meta-Heuristic Optimization for Airplane Design

Intelligent Design Methodology: A Fusion of Fuzzy Logic, Axiomatic Design, and Meta-Heuristic Optimization for Airplane Design

An SQP algorithm based on a hybrid architecture for accelerating optimization of large-scale systems

Geometric design of electric motors using adjoint-based shape optimization

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